Optimizing Facilities Management Through Artificial Intelligence and Digital Twin Technology in Mega-Facilities

Ahmed Mohammed Abdelalim (), Ahmed Essawy, Alaa Sherif, Mohamed Salem, Manal Al-Adwani and Mohammad Sadeq Abdullah
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Ahmed Mohammed Abdelalim: Project Management and Sustainable Construction Program, PMSC Founder, Civil Engineering Department, Faculty of Engineering at Mataria, Helwan University, Cairo P.O. Box 11718, Egypt
Ahmed Essawy: Faculty of Engineering at Mataria, Helwan University, Cairo P.O. Box 11718, Egypt
Alaa Sherif: Faculty of Engineering at Mataria, Helwan University, Cairo P.O. Box 11718, Egypt
Mohamed Salem: Department of Civil Engineering, College of Engineering, Australian University of Kuwait, Safat 13015, Kuwait
Manal Al-Adwani: Adjunct Faculty of Civil & Architectural Engineering, International University of Kuwait (IUK), Ardiya 92400, Kuwait
Mohammad Sadeq Abdullah: Department of Architecture, College of Architecture, Kuwait University, Kuwait City 13060, Kuwait

Sustainability, 2025, vol. 17, issue 5, 1-30

Abstract: Mega-facility management has long been inefficient due to manual, reactive approaches. Current facility management systems face challenges such as fragmented data integration, limited predictive systems, use of traditional methods, and lack of knowledge of new technologies, such as Building Information Modeling and Artificial Intelligence. This study examines the transformative integration of Artificial Intelligence and Digital Twin technologies into Building Information Modeling (BIM) frameworks using IoT sensors for real-time data collection and predictive analytics. Unlike previous research, this study uses case studies and simulation models for dynamic data integration and scenario-based analyses. Key findings show a significant reduction in maintenance costs (25%) and energy consumption (20%), as well as increased asset utilization and operational efficiency. With an F1-score of more than 90%, the system shows excellent predictive accuracy for equipment failures and energy forecasting. Practical applications in hospitals and airports demonstrate the developed ability of the platform to integrate the Internet of Things and Building Information Modeling technologies, shifting facilities management from being reactive to proactive. This paper presents a demo platform that integrates BIM with Digital Twins to improve the predictive maintenance of HVAC systems, equipment, security systems, etc., by recording data from different assets, which helps streamline asset management, enhance energy efficiency, and support decision-making for the buildings’ critical systems.

Keywords: facility management (FM); Building Information Modeling (BIM); internet of things (IoT); digital twin (DT); digital trio; employer information requirement (EIR); Artificial Intelligence (AI) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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